In comparison to past UK energy scenario exercises, which were assessed in Section 3, UKERC Phase 2 had access to and developed a range of energy system models.
Based on the specific guiding questions, different models were chosen and justified for scenario construction. The elastic demand variant of the MARKAL model was used most often for capturing the whole system dynamics and interactions and for evaluating climate change mitigation and other environmental policies (Anandarajah and Strachan, 2010; Ekins et al., 2013). While this model was consistently used for multiple analyses, it was also iteratively updated to include new policy and wider developments (Ekins et al., 2013). The MARKAL model was also extended
methodologically to analyse questions other than those focused on the whole
42 system-related. Usher and Strachan (2012) used a stochastic version of the model to better capture the implications of uncertainties. Kannan (2011) added further temporal detail to MARKAL in order to gain better insights into the electricity dispatch, where high temporal resolution is key. The Cambridge multi-sectoral dynamic model (MDM-E3) was used to address the wider implications of
environmental taxation on the economy. Chaudry et al. (2011) combined three models in order to analyse the interactions between climate change mitigation and energy system resilience. Finally, Watson et al. (2012) conceptually derived four scenarios of CCS deployment, which were based on the branching point framework.
Overall, there is a wide availability of different types of models and approaches. The UKERC Systems Theme members seem to reflectively make their model choices and iteratively extend their models to adapt them to new emerging knowledge and to new questions.
43 Table 3. Summary of the analysed UKERC energy scenarios, 2009-2013; the publications are sorted according to the first author and the year of publication
Authors Year Approach/
model
Focus or guiding question(s)
Examples of explicit scenario interpretation statements *
Anandarajah
“Interactions between RO [Renewable Obligation], RTFO [Renewable Transport Fuel Obligations], and RHP [Renewable Heat Programme] policies drive trade-offs between low carbon electricity, bio-fuels, high efficiency natural gas, and demand reductions as well as resulting 2020 welfare costs” (p.6724) Numbers, such as:
“Under a cost optimal model pathway, existing UK policies and technology options in the Reference Scenario (RS) would
reduce CO2 emissions in 2020 to about 500 MtCO2 and in 2050 to 584 MtCO2.” (p.6727)
Policy implications or decision support, such as:
none
“In all cases, however, the costs of achieving the [carbon]
reductions are relatively modest.” (p.865)
“When CO2 emissions are increasingly constrained… the model strongly decarbonises the electricity sector, and there is a huge change in the capacity mix in the power sector.” (p.871) Numbers, such as:
44 the UK energy policy”
(p.865)
“If no new policies/measures are enacted, energy-related CO2
emissions (in the Base reference scenario (B) in 2050 would be 584 MtCO2, which is 6% higher than the 2000 emission level and only 1% lower than the 1990 emission level.” (p. 869) Policy implications or decision support, such as:
“These model runs reveal the single most important policy priority to be to incentivize the effective decarbonisation of the electricity system <…>. All the low-carbon model runs have substantial quantities of each of these technologies by 2050, indicating that their costs are broadly comparable and that each of them is required for a low-carbon energy future for the UK. The policy implications are clear: all these technologies should be developed.” (p.878)
“Achieving the macro goals of reduced imports and greater supply diversity can be achieved through the vigorous pursuit of fairly conventional policy instruments. The key is a very strong emphasis on policies to improve energy efficiency in buildings and transport.… Keeping up the pace of investment in renewables and nuclear will also contribute.” (Executive summary, point 41)
Numbers, such as:
“Applying these reliability standards adds to electricity system costs. The maximum annual increase across the scenarios is
45
summary, point 2) £354m in 2020 (Low Carbon Resilient scenario), £575m in 2035 (Low Carbon scenario) and £457m in 2050 (Resilient scenario).” (Executive abstract, point 24)
Policy implications or decision support, such as:
“There are three possible models for stimulating such
investment: Government provides the appropriate framework for the market to make the investment; the regulator permits the investment through price reviews, but the investment is provided by the regulated companies; Government carries out the investment itself.” (Executive summary, point 44)
Methodological contributions, such as:
“These results suggest that substantial reductions in GHG emissions can be achieved with minimal impacts on output and an overall increase in employment” (p.447)
Numbers, such as:
“Spending only 10% of the extra tax revenues on green investments results in a further reduction in CO2 emissions from the 1990 level of 3.5% from S1 [ETR scenario] to E1 [Eco-innovation scenario].” (p.472)
Policy implications or decision support, such as:
“In the absence of evidence to the contrary, they suggest that ETR is a very attractive policy indeed” (p.474)
“This leaves ETR as the preferred policy instrument to meet the UK’s GHG emission reduction targets. Other policy instruments
46 may be used to reinforce ETR, or increase the response to the shift in relative prices which it brings about.” (p.473)
Methodological contributions, such as:
“A first obvious observation is that the resilience targets lead to significant emissions reductions, even if no additional policies beyond REF are introduced.” (Page 39)
“Nuclear appears to be the most economically attractive low-carbon option.” (p.50)
Numbers, such as:
“In REF [Reference case] and ADD [Additional policies scenario]
under high gas prices, the carbon intensity of generation falls to 80-90 g/kWh by 2030 driven by the CPF [Carbon price floor] and fall further to about 30 g/KWh by 2050.” (p.41) Policy implications or decision support, such as:
“First, the electricity market reform (EMR) in the Energy Bill 2012 must provide an economically viable transition for gas generators to move from base load to largely back-up generators by 2030.” (p.53)
“On the supply side, hydrogen-based electricity storage is greatly preferred but stored-hydrogen is used in the transport sector rather than for power system balancing mechanism”
47
“On average, the system chooses about 7–10% of electricity demand as storage.” (p.2261)
Policy implications or decision support, such as:
none
Methodological contributions, such as:
“Nonetheless, the model results do revel that the temporal MARKAL sheds powerful insights on the role of demand and supply-side energy storage.” (Page 2270)
Markusson
“Capture readiness comes with serious uncertainties and is no guarantee that new-built fossil plants will be abatable or abated in the future.” (p.6695)
Numbers, such as:
none
Policy implications or decision support, such as:
“We have… shown that the only safe way to avoid further carbon lock-in, until CCS has been developed, is to not build new fossil plants.” (p.6702) the volumes of oil that can and cannot be used up to 2035
Insights, such as:
“The above results demonstrate that large volumes of oil
currently considered to be reserves cannot be produced before 2035 if there is to be an evens chance of limiting the global
48
average temperature rise to 2oC” (p.111) Numbers, such as:
“On a global scale nearly 600 Gb of oil reserves must remain unused by 2035 in a scenario where CCS is unavailable, around 45% of available reserves” (p.111)
Policy implications or decision support, such as:
“The work thus demonstrates the extent to which current energy policies encouraging the unabated exploration for, and exploitation of, all oil resources are incommensurate with the achievement of a low-carbon energy system” (p.102)
“To conclude, a large disconnect appears to exist between policies permitting exploration in new areas, particularly in Arctic and deepwater areas, and pledges to restrict
temperature rises to 2oC. The continued licensing of new areas for oil exploration is only consistent with declared intentions to limit CO2 emissions and climate change if the majority of fields that are discovered remain undeveloped, which fatally undermines the economic rationale for their discovery in the first place.” (p.111)
Methodological contributions, such as:
None Strachan 2011a MARKAL
elastic an integral part of any
Insights, such as:
“Interestingly, in comparing the change from BAuU vs. REF [Reference] cases to the standard vs. high fossil price cases, the two effects give approximately the same order of impact of costs. Thus the inclusion of existing polices in modelling
long-49
term decarbonisation pathways appears to be comparable to a major exogenous modelling assumption — that of global fossil fuel prices.” (p.160)
Numbers, such as:
“By 2050, removing existing policies gives an increase in CO2 marginal costs from £182/tCO2 to £205/tCO2 and in annual welfare loss from £20.6 billion to £25.2 billion.” (p.160) Policy implications or decision support, such as:
none
Methodological contributions, such as:
“Best practice in energy modelling would be to have both a no-policy reference baseline, and a current no-policy reference
baseline (BAuU). At a minimum, energy modelling studies should have a transparent assessment of the current policy contained within the baseline.” (p.153)
“If it is not done, energy models will likely underestimate the true cost of long- term emissions reductions.” (p.160)
Strachan and
“Under a combinatory second-best scenario, meeting targets greater than a 70% reduction in CO2 by 2050 entail costs above a subjective barrier of 1% of GDP, while extreme mitigation scenarios (>90% CO2 reduction) are infeasible.”
(p.121)
Numbers, such as:
“Under a second-best scenario, a 90% CO2 reduction by 2050 requires an economy wide carbon price of £538/tCO2 and
50 incurs an annual welfare cost of £33.7 billion.” (p.136)
Policy implications or decision support, such as:
“For a developed country such as the UK which has positioned itself in the vanguard of global climate mitigation efforts this finding supports the current legislative efforts to plan a long-term decarbonisation pathway” (p, 136)
Methodological contributions, such as:
“By demonstrating the fragilities of a low carbon energy system pathway, policy makers can explore protective and proactive strategies to ensure targets can actually be met.”
(p.121)
“This paper shows that for those uncertain variables that result in divergent near-term actions under perfect information, it is important to make decisions in a manner that take account of the uncertainties, for these uncertainties can be extremely expensive.” (p.443)
Numbers, such as:
“Evaluating the uncertainty under a decarbonisation agenda shows that fossil fuel price uncertainty is very expensive at around £20 billion. The addition of novel mitigation options reduces the value of fossil fuel price uncertainty to £11 billion.” (p,435)
Policy implications or decision support, such as:
None
Methodological contributions, such as:
51
“Stochastic MARKAL is a powerful tool for investigating the complex systemic dynamics of energy focused decision-making under uncertainty” (p.444) a set of pathways were developed for CCS from now to 2030”
(p.33)
Insights, such as:
“A supportive political, policy and financial environment allows CCS projects to be competitive and financed through a
combination of debt and equity” (p. 38) Numbers, such as:
None
Policy implications or decision support, such as:
“To achieve this aim requires comprehensive policy support now. Whilst the CCS roadmap promises such comprehensive support, the commercialisation programme needs to yield firm commitments to build several full scale CCS projects as soon as possible.” (p.43)
“The most attractive low carbon supply technologies – and the research priorities associated with their commercialisation – are sensitive to overall level of decarbonisation ambition.
Raising the decarbonisation ambition from 60% to 80% does not simply mean doing ‘more of the same’ – it introduces new
52
technology preferences and research priorities.” (p.111) Numbers, such as:
“Renewable electricity provides a much greater proportion of primary energy demand by 2050 in accelerated scenarios:
almost 20% in LC Acctech [Low Carbon Accelerated technology scenario] 80, compared to under 5% in LC Core 80 [Low
Carbon Core scenario].” (p.112)
Policy implications or decision support, such as:
“Accelerating the development of emerging low carbon energy supply technologies offers significant long term benefit, in enabling alternative and potentially more affordable
decarbonisation of the UK energy system” (p.136) Methodological contributions, such as:
“The analysis suggests that accelerated development could open up more affordable and more diverse decarbonisation pathways over the longer term.” (p.187)
Numbers, such as:
“Between 2010 and 2050, accelerated technology development provides a total savings in the welfare costs of achieving 80 per cent decarbonisation of £36bn.”
(p.207)